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Linear contextual bandit is a popular online learning problem. It has beenmostly studied in centralized learning settings. With the surging demand oflarge-scale decentralized model learning, e.g., federated learning, how toretain regret minimization while reducing communication cost becomes an openchallenge. In this paper, we study linear contextual bandit in a federatedlearning setting. We propose a general framework with asynchronous model updateand communication for a collection of homogeneous clients and heterogeneousclients, respectively. Rigorous theoretical analysis is provided about theregret and communication cost under this distributed learning framework; andextensive empirical evaluations demonstrate the effectiveness of our solution.

Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits